A Novel Method Based on Data Visual Autoencoding for Time-Series Classification

摘要：

　　A variety of techniques based on numerical characteristics are currently presented for mining time-series data.However,we find that time-series data generally contain curves sharing some set of visual characteristics and features.These characteristics offer a deeper understanding of time-series data,and open up a potential new technique for time-series analysis.Particularly beneficial from recent advances in deep neural networks,representations and features can be automatically learnt by deep learning architectures such as autoencoders.Based on that,our work proposes a novel method,named time-series visualization(TSV),to efficiently detect visual characteristics from curves of time-series data and use these characteristics for intelligent analysis.Architecture and algorithm of TSV based on stacked autoencoders are introduced in this paper.Further,important factors affecting the performance of TSV are discussed based on empirical results.Through empirical evaluation,it is demonstrated that TSV has better efficiency and higher classification accuracy on analyzing the datasets with significant curve feature.

关键词： Time series Autoencoder Classification Input dropout TSV

作者: Chen Qian Yan Wang Lei Guo

作者单位: School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,Peoples Republic of China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 97-104

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）